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Inference of causal interactions among genes known to be involved in the regulation of cell cycle, has received considerable attention in recent years. Capturing the mechanism of gene regulation in the cell cycle is necessary to elucidate both normal and abnormal cell reproduction. Within the last few years, many reverse engineering approaches have been applied to the yeast Saccharomyces cerevisiae. Among these approaches, Dynamic Bayesian Networks (DBNs) are of particular interest. However, learning the structure of these networks is an NP-hard problem. In this paper, we apply DBN with an evolutionary structure learning strategy, M-CMA-ES, to 14 cell cycle regulated genes in the yeast Saccharomyces cerevisiae dataset. The resulting interactions are evaluated and compared with the KEGG pathway as the target network. Precision and sensitivity are also used as evaluation criteria for comparing our inferred network with two previous studies of yeast cell cycle data. The results indicate markedly improved scores for M-CMA-ES approach compared to previous methods.